Analysis of the Content of the Chemistry Book for the Third Intermediate Stage According to Environmental Problems

The current aim of this study is to know Environmental problems which included in the content of chemistry textbook of intermediate third stage : -To achieve this aim, the researcher organized a listing of environmental issues consisting of (8) major areas, namely (air and air pollution, water pollution, soil pollution, energy, disturbance of biodiversity and environmental balance, waste management foods and medication pollution, mineral wealth investment). Of which (60) sub-issues, then the researcher analyzed the chemistry book for the third intermediate stage   for the academic year (2020-2021) in slight of the listing that was prepared, its validity and validity. The consistency of the book became confirmed, and the effects confirmed that the chemistry textbook for the 0.33 grade had a mean of (15) sub-issues, and a percent of ( 25%). In slight of the studies effects, the researcher recommends consisting of the content of the   chemistry textbook for the third intermediate stage and the major and minor environmental issues that aren't available, as one of the current developments in medical education

An object-oriented neural network approach to short-term traffic forecasting

This paper discusses an object-oriented neural network model that was developed for predicting short-term traffic conditions on a section of the Pacific Highway between Brisbane and the Gold Coast in Queensland, Australia. The feasibility of this approach is demonstrated through a time-lag recurrent network (TLRN) which was developed for predicting speed data up to 15 minutes into the future. The results obtained indicate that the TLRN is capable of predicting speed up to 5 minutes into the future with a high degree of accuracy (90-94%). Similar models, which were developed for predicting freeway travel times on the same facility, were successful in predicting travel times up to 15 minutes into the future with a similar degree of accuracy (93-95%). These results represent substantial improvements on conventional model performance and clearly demonstrate the feasibility of using the object-oriented approach for short-term traffic prediction. (C) 2001 Elsevier Science B.V. All rights reserved

Public perception of connected and automated vehicles: Benefits, concerns, and barriers from an Australian perspective

This study investigates the attitudes and concerns of the Australian public toward connected and autonomous vehicles (CAVs), and the factors influencing their willingness to adopt this technology. Through a comprehensive survey, a diverse group of respondents provided valuable insights toward various CAV scenarios such as riding in a vehicle with no driver, self-driving public transport, self-driving taxis, and heavy vehicles without drivers. The results highlight the significant impact of safety concerns about automated vehicles on individuals’ attitudes across all scenarios. Higher levels of concern were associated with more negative attitudes, and a strong correlation between concerns and opposition underlines the necessity of addressing these apprehensions to build public trust and promote CAV adoption. Interestingly, nearly 70% of respondents felt uncomfortable driving next to a CAV, but they displayed more confidence in adopting automated public transport in the near future. Additionally, around 40% of participants indicated a strong willingness to purchase a CAV, primarily driven by the desire to reduce their carbon footprint and safety considerations. Notably, respondents with health conditions or disability exhibited heightened interest (almost double those without health conditions) in CAV technology. Gender differences emerged in attitudes and preferences toward CAVs, with women expressing a greater level of concern and perceiving higher barriers to CAV deployment. This emphasizes the importance of employing targeted approaches to address the specific concerns of different demographics. The study also underscores the role of trust in technology as a significant barrier to CAV deployment, ranking high among respondents’ concerns. To overcome these challenges and facilitate successful CAV deployment, various strategies are suggested, including live demonstrations, dedicated routes for automated public transport, adoption incentives, and addressing liability concerns. The findings from this study offer valuable insights for government agencies, vehicle manufacturers, and stakeholders in promoting the successful implementation of CAVs. By understanding societal acceptance and addressing concerns, decision-makers can devise effective interventions and policies to ensure the safe and widespread adoption of CAVs in Australia. Moreover, vehicle manufacturers can leverage these results to consider design aspects that align with passenger preferences, thereby facilitating the broader acceptance and adoption of CAVs in the future. Finally, this research provides a significant contribution to the understanding of public perception and acceptance of CAVs in the Australian context. By guiding decision-making and informing strategies, the study lays the foundation for a safer and more effective integration of CAVs into the country’s transportation landscape

Decentralised Traffic Incident Detection via Network Lasso

Traffic incident detection plays a key role in intelligent transportation systems, which has gained great attention in transport engineering. In the past, traditional machine learning (ML) based detection methods achieved good performance under a centralised computing paradigm, where all data are transmitted to a central server for building ML models therein. Nowadays, deep neural networks based federated learning (FL) has become a mainstream detection approach to enable the model training in a decentralised manner while warranting local data governance. Such neural networks-centred techniques, however, have overshadowed the utility of well-established ML-based detection methods. In this work, we aim to explore the potential of potent conventional ML-based detection models in modern traffic scenarios featured by distributed data. We leverage an elegant but less explored distributed optimisation framework named Network Lasso, with guaranteed global convergence for convex problem formulations, integrate the potent convex ML model with it, and compare it with centralised learning, local learning, and federated learning methods atop a well-known traffic incident detection dataset. Experimental results show that the proposed network lasso-based approach provides a promising alternative to the FL-based approach in data-decentralised traffic scenarios, with a strong convergence guarantee while rekindling the significance of conventional ML-based detection methods

Modeling Operating Speed Using Continuous Speed Profiles on Two-Lane Rural Highways in India

The geometric elements of the road, such as tangents and curves, play a vital role in road safety because significant crashes are reported on the horizontal curves and tangent-to-curve transitions. Literature reveals that inconsistent geometric design of roads violates driver's expectation of operating speed leading to crashes. For safe manoeuver, it is necessary to achieve consistent operating speed with road geometry based on the driver's expectations rather than the designer's perception. Estimation of reliable operating speeds in the design phase will help to design safer road alignments. Several past studies developed operating speed models on the curves and tangent-to-curve transitions. However, these models used spot speed data with the assumption that the constant speed persists on the horizontal curves and entire deceleration/acceleration occurs on the approach/departure tangents. In this study, an instrumented vehicle with a high-end GPS (global positioning system) device was used to obtain the continuous speed profiles for passenger cars which resulted in reliable and robust peed prediction models on a tangent, curve, and tangent-to-curve. to speed prediction models for a tangent, curve, and tangent-to-curve. The study also establishes a relationship between the differential of the 85th percentile speed (Î"V85) and 85th percentile speed differential (Î"85V). The analysis results revealed that Î"V85 underestimates Î"85V by 5.32 km/h, and Î"85V predicted the actual speed reduction from tangent-to-curve transitions. Statistical analysis results showed low errors, variations, and strong correlation of the proposed models with the field data. The models developed in the present study were validated and compared with various other models available in the literature. The comparative study highlights the importance of using continuous speed profile data to calibrate the operating speed models. © 2020 American Society of Civil Engineers

Training Physics-Informed Neural Networks via Multi-Task Optimization for Traffic Density Prediction

Physics-informed neural networks (PINNs) are a newly emerging research frontier in machine learning, which incorporate certain physical laws that govern a given data set, e.g., those described by partial differential equations (PDEs), into the training of the neural network (NN) based on such a data set. In PINNs, the NN acts as the solution approximator for the PDE while the PDE acts as the prior knowledge to guide the NN training, leading to the desired generalization performance of the NN when facing the limited availability of training data. However, training PINNs is a non-trivial task largely due to the complexity of the loss composed of both NN and physical law parts. In this work, we propose a new PINN training framework based on the multi-task optimization (MTO) paradigm. Under this framework, multiple auxiliary tasks are created and solved together with the given (main) task, where the useful knowledge from solving one task is transferred in an adaptive mode to assist in solving some other tasks, aiming to uplift the performance of solving the main task. We implement the proposed framework and apply it to train the PINN for addressing the traffic density prediction problem. Experimental results demonstrate that our proposed training framework leads to significant performance improvement in comparison to the traditional way of training the PINN.Comment: accepted by the 2023 IEEE International Joint Conference on Neural Networks (IJCNN 2023

Punching Shear Characterization of Steel Fiber-Reinforced Concrete Flat Slabs

Punching shear failure in thin slabs under concentrated loads can cause shear stresses near columns. The use of steel fiber is a practical way to improve a slab-column connection's punching strength and deformation capacity. In this study, the capacity and behavior of steel fiber-reinforced concrete flat slabs are examined under punching shear force. Ten small-scale flat slabs were tested, eight of which had steel fiber and two without. Two parameters are studied in this paper, which are the fiber volume ratio (from 0% to 2%) and the stub column load shape (circle and square). The test results include the concrete compressive strength, crack patterns, punching shear, and load-defection behavior of the slabs. Based on the experimental results, it was found that the punching shear capacity of slabs with steel fiber (S5) increased by 21.8% compared to slabs without steel fiber (S1), and the slabs with steel fiber had more ductility compared to the slabs without fiber. Doi: 10.28991/HIJ-2022-03-04-08 Full Text: PD

Modeling Acceleration and Deceleration Rates for Two-Lane Rural Highways Using Global Positioning System Data

Several past studies developed acceleration/deceleration rate models as a function of a single explanatory variable. Most of them were spot speed studies with speeds measured at specific locations on curves (usually midpoint of the curve) and tangents to determine acceleration and deceleration rates. Fewer studies adopted an estimated value of 0.85 m/s2 for both deceleration and acceleration rates while approaching and departing curves, respectively. In this study, instrumented vehicles with a high-end GPS (global positioning system) device were used to collect the continuous speed profile data for two-lane rural highways. The speed profiles were used to locate the speeds at the beginning and end of deceleration/acceleration on the successive road geometric elements to calculate the deceleration/acceleration rate. The influence of different geometric design variables on the acceleration/deceleration rate was analysed to develop regression models. This study also inspeced the assumption of constant operating speed on the horizontal curve. The study results indicated that mean operating speeds measured at the point of curvature (PC) or point of tangency (PT), the midpoint of curve (MC), and the end of deceleration in curve were statistically different. Acceleration/deceleration rates as a function of different geometric variables improved the accuracy of models. This was evident from model validation and comparison with existing models in the literature. The results of this study highlight the significance of using continuous speed profile data to locate the beginning and end of deceleration/acceleration and considering different geometric variables to calibrate acceleration/deceleration rate models. © 2021 Vinayak Malaghan et al

Inflammatory immune mediators and Plasmodium falciparum infection: a cross-sectional study among Sudanese patients with severe and uncomplicated malaria

Aim: A number of questions remain unanswered concerning how infected individuals regulate their immune response to Plasmodium falciparum (P. falciparum) parasites at varying levels of exposure. Due to the interactions of inflammatory mediators and cytokines with the P. falciparum parasite complex density, several mediators influence parasitaemia and may give some indications of disease severity and represent effective signs in clinical manifestations of malaria disease. Methods: In this study, various levels of immune response mediators of interleukin 8 (IL-8), tumor necrosis factor-beta (TNF-β, also known as lymphotoxin-α), interferon-gamma (IFN-γ), IL-6, and IL-10 were investigated to the different phases of infection with P. falciparum in hyperendemic states in Sudan (White Nile, Blue Nile). This study vetted the association between certain inflammatory mediators during malaria infection and parasite density. This study was based on a total of 108 cases, in which 86 patients (62.0%) were uncomplicated and (17.6%) were severe, all met the diagnostic criteria and were clinically admitted for malaria infections. Commercial enzyme-linked immunosorbent assay (ELISA) kits were employed to determine the inflammatory mediator’s serum concentration. Results: The analysis of data indicated that older infected children had substantially raised levels of IFN-γ (P < 0.05), among study groups, levels of IFN-γ, TNF-β, and IL-8 were strongly linked with the severity of malaria, in severe and uncomplicated cases (P < 0.001), IL-6 and IL-10 were significantly associated with severe malaria cases uniquely (P < 0.001). Furthermore, we reported a positive correlation between IL-8 and TNF-β during all infection cases (r = 0.760, P < 0.001). Additionally, in severe malaria cases IL-6 was positively correlated with IL-10 (r = 0.575, P = 0.010). Conclusions: Eliminating P. falciparum blood-stage infection needs effective, specific, and tuned immune response strategies, which may present in the mediator’s correlations and depend on the density of the infection. Besides the effective levels contribution of certain cytokines that play protective roles during different stages of an infection

Theileria lestoquardi in Sudan is highly diverse and genetically distinct from that in Oman

Malignant ovine theileriosis is a severe tick-borne protozoan disease of sheep and other small ruminants which is widespread in sub-Saharan Africa and the Middle East. The disease is of considerable economic importance in Sudan as the export of livestock provides a major contribution to the gross domestic product of this country. Molecular surveys have demonstrated a high prevalence of sub-clinical infections of Theileria lestoquardi, the causative agent, among small ruminants. No information is currently available on the extent of genetic diversity and genetic exchange among parasites in different areas of the country. The present study used a panel of T. lestoquardi specific micro- and mini-satellite genetic markers to assess diversity of parasites in Sudan (Africa) and compared it to that of the parasite population in Oman (Asia). A moderate level of genetic diversity was observed among parasites in Sudan, similar to that previously documented among parasites in Oman. However, a higher level of mixed-genotype infection was identified in Sudanese animals compared to Omani animals, consistent with a higher rate of tick transmission. In addition, the T. lestoquardi genotypes detected in these two countries form genetically distinct groups. The results of this work highlight the need for analysis of T. lestoquardi populations in other endemic areas in the region to inform on novel approaches for controlling malignant theileriosis